What Is the Model Context Protocol?

The Model Context Protocol (MCP) is a way to let AI systems, like LLMs, connect to the outside world. With MCP, AI can access newest information, trigger actions, or interact with other systems. Instead of buiding a custom connection for every app, database, or API, MCP provides a universal way to do this.

MCP acts like a universal adapter. It defines a common protocol, or a standard, for how AI applications communicate with external tools and data sources. Just as USB-C standardizes how devices connect, MCP standardizes how AI connects to external capabilities. For deepset AI Platform, this means you can connect pipelines to a wide range of tools and services with the same interface.

MCP defines how an AI assistant requests data from tools and how those tools respond. When a tool supports MCP, you can use it across any compatible client, such as Cursor, Claude Desktop, or your own custom agent.

Key Concepts

MCP uses a client-server architecture with three key components:

• MCP Host: The AI app the user interacts with, like Claude Desktop or VS Code. The host manages the overall workflow and launches one MCP client for each server it connects to.
• MCP Client: The component that manages communication between the host and the server.
• MCP Server: A program that offers capabilities, like tools, resources, or prompts, to the client or host. It can run locally or remotely.

The host coordinates the entire process. It creates one MCP client to communicate with each MCP server.

The Workflow

Here's how a typical interaction works:

If the host is Claude Desktop with an MCP extension, and a user asks a question that requires an external tool, the host forwards the query to its MCP client. The client starts a session with the MCP server. They exchange information about available tools and capabilities. The client sends a tool call, the server returns the result, and the client passes the response back to the host. The host then formats it into natural language for the user.

Benefits of MCP

MCP brings several key benefits to your AI applications.

Vendor-Agnostic Integration

MCP abstracts away vendor-specific APIs, making it possible to connect to external tools and services without writing custom integration code. MCP handles the communication protocol, so you focus on building your AI logic.

Flexible Tool Access

Access a growing ecosystem of MCP-compatible tools, including:

• Database connections
• Web search capabilities
• File system operations
• API integrations
• Custom business tools

Enhanced AI Capabilities

Give your AI models access to real-time data and tools, enabling them to:

• Answer questions with current information
• Perform actions on external systems
• Access specialized data sources
• Execute complex workflows

Future-Proof Architecture

As more tools adopt MCP, your pipelines automatically gain access to new capabilities without code changes.